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    Wyświetlanie 1-3 z 3
    Tytuł:
    Decoupling control for permanent magnet in-wheel motor using internal model control based on back-propagation neural network inverse system
    Autorzy:
    Li, Y.
    Zhang, B.
    Xu, X.
    Powiązania:
    https://bibliotekanauki.pl/articles/200933.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    electric vehicle
    permanent magnet in-wheel motor
    back-propagation neural network
    inverse system
    internal model control
    pojazd elektryczny
    silnik z napędem na magnesy stałe
    inwersja systemu
    propagacja wsteczna
    model odwrotny
    system odwrotny
    Opis:
    The permanent magnet in-wheel motor (PMIWM) is a nonlinear, multivariable, strongly coupled and highly complex system. The key to the development and application of the PMIWM consists in the improvement of its control accuracy and dynamic performance. In order to effectively decouple the PMIWM, this paper presents a novel internal model control (IMC) approach based on the back-propagation neural network inverse (BPNNI) control method. First, theoretical analysis is conducted to show the existence of the PMIWM inverse system, to be modeled mathematically. The inverse system approximated and identified by the back-propagation neural network (BPNN) constitutes the back-propagation neural network inverse (BPNNI) system. Then, by cascading the BPNNI system on the left side of the original PMIWM system, a new decoupling, pseudo-linear system is established. Moreover, the 2-DOF internal model control (IMC) method is employed to design the extra closed-loop controller that further improves disturbance rejection and robustness of the whole system. Consequently, the proposed decoupling control approach incorporates the advantages of both the BPNNI and the IMC. Effectiveness of thus proposed control approach is verified by means of simulation and real-time hardware-in-the-loop (HIL) experiments.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 961-972
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Non-Invasive Hemoglobin Monitoring Device Using K-Nearest Neighbor and Artificial Neural Network Back Propagation Algorithms
    Autorzy:
    Munadi, R.
    Sussi, S.
    Fitriyanti, N.
    Ramadan, D. N.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055237.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    invasive
    non-invasive
    k-nearest neighbor
    artificial neural network
    back propagation
    Opis:
    The invasive method of medically checking hemoglobin level in human body by taking the blood sample of the patient requiring a long time and injuring the patient is seen impractical. A non-invasive method of measuring hemoglobin levels, therefore, is made by applying the K-Nearest Neighbor (KNN) algorithm and the Artificial Neural Network Back Propagation (ANN-BP) algorithm with the Internet of Things-based HTTP protocol to achieve the high accuracy and the low end-to-end delay. Based on tests conducted on a Noninvasive Hemoglobin measuring device connected to Cloud Things Speak, the prediction process using algorithm by means of Python programming based on Android application could work well. The result of this study showed that the accuracy of the K-Nearest Neighbor algorithm was 94.01%; higher than that of the Artificial Neural Network Back Propagation algorithm by 92.45%. Meanwhile, the end-to-end delay was at 6.09 seconds when using the KNN algorithm and at 6.84 seconds when using Artificial Neural Network Back Propagation Algorithm.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 1; 13--18
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives
    Autorzy:
    Sedhuraman, K.
    Himavathi, S.
    Muthuramalingam, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/141220.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sensorless indirect vector controlled IM drives
    speed estimator
    reactive power
    MRAS
    neural network
    back propagation algorithm
    Opis:
    This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.
    Źródło:
    Archives of Electrical Engineering; 2013, 62, 1; 25-41
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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